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Novel CagA ELISA exhibits enhanced sensitivity of Helicobacter pylori CagA antibody

World Journal of Gastroenterology 2017年第1期23卷 48-59页

作者： Yuichi Matsuo Yasutoshi Kido Junko Akada Seiji Shiota Tran Thanh Binh Tran Thi Huyen Trang Ho DQ Dung Pham Huu Tung Tran Dinh Tri Ngo P Minh Thuan Le Quang Tam bui chi nam Vu Van Khien Yoshio Yamaoka Department of Environmental and Preventive Medicine Oita University Faculty of Medicine Department of General Medicine Oita University Faculty of Medicine Department of Endoscopy Cho Ray Hospital Department of Molecular Biology 108 Military Central Hospital Department of Endoscopy Daklak Hospital Department of Endoscopy Lao Cai Hospital Department of Hepatogastroentrology 108 Military Center Hospital Department of Medicine-Gastroenterology Baylor College of Medicine and Michael E.De Bakey Veterans Affairs Medical Center

AIMTo develop a novel Helicobacter pylori (H. pylori) CagA antibody enzyme-linked immunosorbent assay (ELISA) suitable for detecting serum anti-CagA antibodies with high *** East Asian-type CagA protein was purified and immobilized for ELISA. Serum samples from 217 Vietnamese individuals (110 H. pylori-infected and 107 uninfected individuals) were applied. Conventional ELISA from Western-type CagA and our East Asian-type CagA ELISA were evaluated by comparing 38 subjects with the Western-type genotype and 72 subjects with the East Asian-type cagA genotype. Histological scores of the gastric mucosa were determined using the updated Sydney System to examine the relationship with anti-CagA antibody *** 70-100 kDa fragments were immobilized on the ELISA plate. In ROC analysis, the area under the curve of our East Asian-type CagA ELISA was comparable to that of conventional CagA ELISA. The sensitivity of the two ELISAs differed depending on the cagA genotype. The sensitivity of East Asian-type CagA ELISA was higher for subjects infected with East Asian-type cagA H. pylori (P < 0.001), and the sensitivity of the conventional CagA ELISA tended to be higher for subjects infected with Western cagA H. pylori (P = 0.056). The titer of anti-CagA antibody tended to correlate with monocyte infiltration scores (r = 0.25, P = 0.058) and was inversely correlated with H. pylori density (r = -0.26, P = 0.043).CONCLUSIONThe novel ELISA is useful to detect anti-CagA antibodies in East Asian countries, and the titer may be a marker for predicting chronic gastritis.

关键词： Helicobacter pylori cagA genotype Anti-CagA antibody Enzyme-linked immunosorbent assay Inflammation Gastritis

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Novel Soft ComputingModel for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

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Computer Modeling in Engineering & Sciences 2023年第3期134卷 2227-2246页

作者： Quang-Hieu Tran Hoang Nguyen Xuan-nam bui Department of Surface Mining Mining FacultyHanoi University of Mining and GeologyHanoi100000Vietnam Innovations for Sustainable and Responsible Mining(ISRM)Research Group Hanoi University of Mining and GeologyHanoi100000Vietnam

This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning ***,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial *** bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and *** is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as *** addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive *** this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also *** were then compiled as the dataset to develop the PPV predictive *** results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR *** those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.

关键词： Mine blasting blast-induced ground vibration environmentally friendly blasting peak particle velocity bagging extra trees

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A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

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Frontiers of Structural and Civil Engineering 2022年第6期16卷 667-684页

作者： Nang Duc bui Hieu chi PHAN Tiep Duc PHAM Ashutosh Sutra DHAR Institute of Techniques for Special Engineering Le Quy Don Technical UniversityHanoi 100000Vietnam Faculty of Engineering and Applied Science Memorial University of NewfoundlandSt.John’sNL A1B 3X5Canada

The study proposes a framework combining machine learning(ML)models into a logical hierarchical system which evaluates the stability of the sheet wall before other *** study uses the hardening soil(HS)model to develop a 200-sample finite element analysis(FEA)database,to develop the ML ***,a system containing three trained ML models is proposed to first predict the stability status(random forest classification,RFC)followed by 1)the cantilever top horizontal displacement of sheet wall(artificial neural network regression models,RANN1)and 2)vertical settlement of soil(RANN2).The uncertainty of this data-driven system is partially investigated by developing 1000 RFC models,based on the application of random sampling technique in the data splitting *** on the distribution of the evaluation metrics reveals negative skewed data toward the 1.0000 *** implies a high performance of RFC on the database with medians of accuracy,precision,and recall,on test set are 1.0000,1.0000,and 0.92857,*** regression ANN models have coefficient of determinations on test set,as high as 0.9521 for RANN1,and 0.9988 for RANN2,*** parametric study for these regressions is also provided to evaluate the relative insight influence of inputs to output.

关键词： finite element analysis cantilever sheet wall machine learning artificial neural network random forest

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Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1452-1465页

作者： Chengyu Xie Hoang Nguyen Xuan-nam bui Van-Thieu Nguyen Jian Zhou School of Environment and Resources Xiangtan UniversityXiangtan411105China Mining Faculty Hanoi University of Mining and GeologyHanoi100000Viet Nam Innovations for Sustainable and Responsible Mining(ISRM)Group Hanoi University of Mining and GeologyHanoi100000Viet Nam Artificial Intelligence Independent Research Group Hanoi100000Viet Nam School of Resources and Safety Engineering Central South UniversityChangsha410083China

Due to the rapid industrialization and the development of the economy in each country,the demand for energy is increasing *** coal mines have to pace up the mining operations with large production to meet the energy *** requirement has led underground coal mines to go deeper with more difficult conditions,especially the mining hazards,such as large deformations,rockburst,coal burst,roof collapse,to name a ***,this study aims at investigating and predicting the stability of the roadways in underground coal mines exploited by longwall mining method,using various novel intelligent techniques based on physics-based optimization algorithms(***-verse optimizer(MVO),equilibrium optimizer(EO),simulated annealing(SA),and Henry gas solubility optimization(HGSO)) and adaptive neuro-fuzzy inference system(ANFIS),named as MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSOANFIS ***,162 roof displacement events were investigated based on the characteristics of surrounding rocks,such as cohesion,Young’s modulus,density,shear strength,angle of internal friction,uniaxial compressive strength,quench durability index,rock mass rating,and tensile *** MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSO-ANFIS models were then developed and evaluated based on this dataset for predicting roof displacements in roadways of underground *** results indicated that the proposed intelligent techniques could accurately predict the roof displacements in roadways of underground mines with an accuracy in the range of 83%-92%.Remarkably,the SA-ANFIS model yielded the most dominant accuracy(i.e.92%).Based on the accurate predictions from the proposed techniques,the reinforced solutions can be timely suggested to ensure the stability of roadways during exploiting coal,especially in the underground coal mines exploited by the longwall mining.

关键词： Roof displacement Longwall mining Underground mine Physics-based optimization Risk assessment Mining hazards

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Investigating the customer trust in artificial intelligence: The role of anthropomorphism, empathy response, and interaction

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 260-273页

作者： Nguyen Thi Khanh chi nam Hoang Vu Foreign Trade University HanoiVietnam Faculty of International Economics Foreign Trade UniversityHanoiVietnam

Artificial intelligence(AI)applications are observed in different industries including the services,where the impressive changing pace is boosted by the technological advances in *** use of AI in the daily work of individuals and in shaping the relationship between firms and customers has resulted in increased efficiency and a higher degree of fascination in *** are,however,customers'fears related to the use of ***,understanding how AI affects customer trust is worth *** study focuses on the AI applications and their impact on communication quality and customer *** features of AI applications investigated in this study include anthropomorphism,empathy response and interaction.A model was first empirically validated through Confirmatory Factor Analysis and tested using Covariance-Based Structural Equation *** were then collected from a survey of 507 customers of two banks and two big telecommunication firms in *** results indicate that anthropomorphism and interaction do not play critical roles in generating customer trust in AI unless they create communication quality with *** ability of empathy response makes customers trust in various service *** application seems to be a good solution for service firms to enhance their relationship with customers in difficult circumstances,including a pandemic *** study delivers implications for enhancing AI adoption in the service firms that intend to innovate their future operations.

关键词： AI anthropomorphism empathy response future orientation interaction

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Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

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Geoscience Frontiers 2021年第3期12卷 458-472页

作者： Chengyu Xie Hoang Nguyen Xuan-nam bui Yosoon Choi Jian Zhou Thao Nguyen-Trang School of Environment and Resources Xiangtan UniversityXiangtan 411105China Institute of Research and Development Duy Tan UniversityDa Nang 550000Viet Nam Department of Surface Mining Mining FacultyHanoi University of Mining and Geology18 Pho VienDuc Thang wardBac Tu Liem districtHanoi 100000Viet Nam Center for Mining Electro-Mechanical researchHanoi University of Mining and Geology18 Pho VienDuc Thang wardBac Tu Liem districtHanoi 100000Viet Nam Department of Energy Resources Engineering Pukyong National UniversityBusan 48513Republic of Korea School of Resources and Safety Engineering Central South UniversityChangsha 410083HunanChina Division of Computational Mathematics and Engineering Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh City 700000Viet Nam Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh City 700000Viet Nam

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit *** evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental ***,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open ***,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,***,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity *** developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop *** which,111 events were randomly selected for the development and optimization of the ***,the remaining 25 blasting events were applied to confirm the accuracy of the proposed ***,blast design parameters were regarded as input variables to predict the size of rock in blasting ***,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench *** the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented *** other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and ***,the FFA-GBM model can be used as a powerful

关键词： Mine blasting Rock fragmentation Artificial intelligence Hybrid model Gradient boosting machine Meta-heuristic algorithm

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Suzuki Reaction of Aryl Bromides Using a Phosphine-Free Magnetic Nanoparticle-Supported Palladium Catalyst

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催化学报 2011年第11期32卷 1667-1676页

作者： Nghia T. bui Trung B. DANG Ha V. LE nam T. S. PHAN Department of Chemical Engineering HCMC University of Technology VNU-HCM 268 Ly Thuong Kiet Distriet 10 Ho Chi Minh City Viet Nam

A palladium catalyst immobilized on superparaganetic nanoparticles was prepared with a palladium loading of 0.30 mmol/g. The catalyst was characterized using X-ray diffraction, scanning electron microscopy, transmission electron microscopy, vibrating sample magnetometry, thermogravimetric analysis, Fourier transform infrared, atomic absorption spectrophotometry, and nitrogen adsorption. The immobilized palladium catalyst was an efficient catalyst without added phosphine ligands for the Suzuki cross-coupling reaction of several aryl bromides with phenylboronic acid. The recovery of catalyst was simply by magnetic decantation in the presence of a magnet. The immobilized palladium catalyst can be reused many times without significant degradation in catalytic activity. No leaching of active palladium species into the reaction solution was detected.

关键词： superparamagnetic nanoparticle Suzuki reaction palladium aryl bromide

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Automatic Discontinuity Classification of Wind-turbine Blades Using A-scan-based Convolutional Neural Network

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Journal of Modern Power Systems and Clean Energy 2021年第1期9卷 210-218页

作者： Jiyeon Choung Sun Lim Seung Hwan Lim Su Chung chi Mun Ho nam Korea Electronics Technology Institute 401-402Bucheon Technopark655Pyeongcheon-roWonmi-guBucheon-siGyeonggi-do 14502Korea Sam Yong Inspection Engineering Co.Ltd.

Recent development trends in wind power generation have increased the importance of the safe operation of wind-turbine blades(WTBs). To realize this objective, it is essential to inspect WTBs for any defects before they are placed into operation. However, conventional methods of fault inspection in WTBs can be rather difficult to implement, since complex curvatures that characterize the WTB structures must ensure accurate and reliable inspection. Moreover, it is considered useful if inspection results can be objectively and consistently classified and analyzed by an automated system and not by the subjective judgment of an inspector. To address this concern,the construction of a pressure-and shape-adaptive phased-array ultrasonic testing platform, which is controlled by a nanoengine operation system to inspect WTBs for internal defects, has been presented in this paper. An automatic classifier has been designed to detect discontinuities in WTBs by using an A-scanimaging-based convolutional neural network(CNN). The proposed CNN classifier design demonstrates a classification accuracy of nearly 99%. Results of the study demonstrate that the proposed CNN classifier is capable of automatically classifying the discontinuities of WTB with high accuracy, all of which could be considered as defect candidates.

关键词： Wind-turbine blade(WTB) blade inspection platform convolutional neural network(CNN) discontinuity phased-array ultrasonic testing(PAUT) A-scan

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Deduction of a meteorological phenology indicator from reconstructed MODIS LST imagery

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Journal of Forestry Research 2020年第6期31卷 2205-2216页

作者： chi Hong Lim Song Hie Jung nam Shin Kim Chang Seok Lee Department of Bio and Environmnetal Technology Graduate SchoolSeoul Women’s UniversitySeoul Republic of Korea Research Team on Ecological and Natural Map National Institute of EcologySeocheon Choongnam Republic of Korea Department of Bio and Environmnetal Technology Seoul Women’s UniversitySeoul Republic of Korea

Phenology is a valuable attribute of vegetation to assess the biological impacts from climate change.A challenge of phenological research is to obtain information on both high temporal resolution and fine spatial scale ***,we constructed an air temperature map based on temporal merging and spatial interpolation algorithms to overcome the cloud-related problem from the MODIS LST ***,we derived the accumulated growing degree days(AGDD)from the constructed mean air temperature map to use as a meteorological ***,we verified the indicator with the seasonal mean air temperature and the green-up date of a Quercus mongolica forest determined from the field-based *** AGDD threshold for each *** forest when the first leaf has unfolded was detected from the EXG trajectory extracted from digital camera images.A comparison between meteorological and MODIS-derived AGDD showed good agreement between *** was also high consistency between DoYs extracted from AGDD and EVI based on curvature K for *** forests of 30 sampling sites throughout South *** results prove that microclimatic factors such as elevation,waterbody,and land-use intensity were faithfully reflected in the reconstructed ***,the results of this study could be applied effectively in areas where microclimatic variation is very severe and for monitoring phenology of undergrowth,which is difficult to detect from reflectance imaging.

关键词： Climate change Digital camera Growing degree days MODIS Phenology Quercus mongolica

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Revisiting the Climatology of Atmospheric Blocking in the Northern Hemisphere

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Advances in Atmospheric Sciences 2013年第2期30卷 397-410页

作者： Ho nam CHEUNG 周文 Hing Yim MOK Man chi WU Yaping SHAO City University of Hong Kong Shenzhen Research Institute Guy Carpenter Asia-Pacific Climate Impact Centre School of Energy and Environment City University of Hong Kong Hong Kong Observatory Institute for Geophysics and Meteorology University of Cologne

In addition to the occurrence of atmospheric blocking, the climatology of the characteristics of blocking events, including duration, intensity, and extension, in four seasons over the Northern Hemisphere was analyzed for the period 1950-2009. The seasonality and spatial variations of these characteristics were studied according to their longitudinal distributions. In general, there were sharp discrepancies in the blocking characteristics between winter and summer, and these differences were more prominent over the Atlantic and Pacific Oceans. The blocking not only occurred more frequently but also underwent stronger amplification in winter; likewise, the blocking occurred less frequently and underwent weaker amplification in summer. There are very strong interrelationships among different blocking characteristics, suggesting that they are supported by similar physical factors. In addition, the relationship between blocking over different regions and East Asian circulation was examined. Ural-Siberia is a major blocking formation region in all seasons that may exert a downstream impact on East Asia. The impact is generally weak in summer, which is due to its lower intensity and smaller duration. However, the extratropical circulation over East Asia in summer can be disturbed persistently by the frequent occurrence of blocking over the Asian continent or the Western Pacific. In particular, the blocking frequency over the Western Pacific significantly increased during the study period. This climatological information provides a background for studying the impact of blocking on East Asian circulation under both present and future climate conditions.

关键词： atmospheric blocking climatology seasonality East Asian circulation

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